Healthcare Operations Management India 2011

2011 ◽  
Vol 6 (2) ◽  
pp. 22-22
Author(s):  
Aliyyah Nuha Faiqah
Keyword(s):  
Operations Management ◽  
Healthcare Operations ◽  
Healthcare Operations Management

Co-Creation and Healthcare Operations Management

2017 ◽  
pp. 400-414 ◽  
Author(s):  
Paul Lillrank
Keyword(s):  
Health Service ◽  
Operations Management ◽  
Service Research ◽  
Healthcare Operations ◽  
Service Production ◽  
Healthcare Operations Management

Service research has produced a definition that sees services as the integration of customers' and producers' resources to co-create value. Clear articulation of hitherto obscure phenomena enables sharper thinking on how such phenomena could be managed. This article discusses the implications of co-creation in healthcare, a sector of society that is perceived as difficult to manage. Co-creation is here understood as a variable that has different intensity and significance in different areas of healthcare. The Demand – Supply –based operating logic (DSO) is used to segment health service production into areas where co-creation appears in different roles.

Surgery Sequencing Coordination with Recovery Resource Constraints

2021 ◽  
Author(s):  
Miao Bai ◽  
Robert H. Storer ◽  
Gregory L. Tonkay
Keyword(s):  
Operations Management ◽  
Resource Constraints ◽  
Postanesthesia Care Unit ◽  
Healthcare Operations ◽  
Healthcare Leadership ◽  
Solution Approach ◽  
Algorithmic Solution ◽  
Healthcare Operations Management ◽  
Highly Correlated ◽  
The Impact

Surgical practice administrators need to determine the sequence of surgeries and reserved operating room (OR) time for each surgery in the surgery scheduling process. Both decisions require coordination among multiple ORs and the recovery resource in the postanesthesia care unit (PACU) in a surgical suite. Although existing studies have addressed OR time reservation, surgery sequencing coordination is an open challenge in the stochastic surgical environment. In this paper, we propose an algorithmic solution to this problem based on stochastic optimization. The proposed methodology involves the development of a surrogate objective function that is highly correlated with the original one. The resulting surrogate model has network-structured subproblems after Lagrangian relaxation and decomposition, which makes it easier to solve than the impractically difficult original problem. We show that our proposed approach finds near-optimal solutions in small instances and outperforms benchmark methods by 13%–51% or equivalently an estimated saving of $760–$7,420 per day in surgical suites with 4–10 ORs. Our results illustrate a mechanism to alleviate congestion in the PACU. We also recommend that practice administrators prioritize sequencing coordination over the optimization of OR time reservation in an effort for performance improvement. Furthermore, we demonstrate how administrators should consider the impact of sequencing decisions when making strategic capacity adjustments for the PACU. Summary of Contribution: Our work provides an algorithmic solution to an open question in the field of healthcare operations management. This solution approach involves formulating a surrogate optimization model and exploiting its decomposability and network-structure. In computational experiments, we quantitatively benchmark its performance and assess its benefits. Our numerical results provide unique managerial insights for healthcare leadership.

Improving healthcare operations management with machine learning

2020 ◽  
Vol 2 (5) ◽  
pp. 266-273 ◽  
Author(s):  
Oleg S. Pianykh ◽  
Steven Guitron ◽  
Darren Parke ◽  
Chengzhao Zhang ◽  
Pari Pandharipande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Operations Management ◽  
Healthcare Operations ◽  
Healthcare Operations Management

OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research

2020 ◽  
Vol 22 (5) ◽  
pp. 869-887 ◽  
Author(s):  
Tinglong Dai ◽  
Sridhar Tayur
Keyword(s):  
Operations Research ◽  
Operations Management ◽  
Healthcare Delivery ◽  
Information Economics ◽  
Organization Design ◽  
Healthcare Operations ◽  
Service Operations Management ◽  
Graph Mapping ◽  
Healthcare Operations Management ◽  
New Generation

A new generation of healthcare operations management (HOM) scholars is studying timely healthcare topics (e.g., organization design, design of delivery, and organ transplantation) using contemporary methodological tools (e.g., econometrics, information economics, and queuing games). A distinguishing feature of this stream of work is that it explicitly incorporates behavior, incentive, and policy considerations arising from the entanglements across multiple entities that make up the complex healthcare ecosystem. This focus is a departure from an earlier generation of research that primarily centered on optimizing given operations of a single entity. This paper provides an introduction to this burgeoning field and maps out research opportunities. We start with identifying key entities of healthcare delivery, financing, innovation, and policymaking, illustrating them on a healthcare ecosystem map (HEM). Next, we explore the HOM literature examining the interactions among various entities in the HEM. We then develop a taxonomy for the recent HOM literature (published in Manufacturing & Service Operations Management, Management Science, and Operations Research between 2013 and 2017), provide a tool-thrust graph mapping methodological tools with research thrusts, and situate the HOM literature in context by connecting it with perspectives from medical journals and mass media. We close with a reference to technological innovations that have the potential to transform the healthcare ecosystem in future decades.

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